Following [1]}, we apply the common approaches in the mixing process and also the addition of pink noise as our first augmentation method. The pink noise is employed to simulate the background noise seen in real recordings. The augmentation parameters are shown in Table REF . For each original stem, we set a 30% probability to apply a specific process (e.g. equalization). The pink noise is applied using colorednoise, and the others are applied using a python package called pysox [2]}. In what follows, we also refer to a model trained with this data augmentation method (i.e., the `Wet' method) as the Wet model.
